Preemptive Prediction-Based Placement of Time-Critical SFCs With VNF Sharing at the Edge

The demand for ultra-low latency requirements is fueled by the growing popularity of real-time and time-critical applications such as virtual, augmented, and mixed reality, and industrial IoT. Time-critical applications and services are real-time software whose failure could result in catastrophic consequences such as fatalities, damage to property, and even financial losses. Edge computing is the main enabler of 5G ultra-low latency use cases. Edge resources are limited compared to abundant cloud computing resources. As such, provisioning time-critical applications at the edge is more challenging and demanding. Even though virtual network function (VNF) sharing improves the utilization of the service providers’ resources, service requests, including time-critical ones, can still be rejected due to insufficient resources. This paper proposes a Preemptive Prediction-based Placement scheme (PPPS) for time-critical services with VNF sharing. In addition to prioritizing timecritical premium (Pr) services over best-effort (BE) services, PPPS utilizes the predicted required resources in a defined lookahead window. In cases when no resources are available for Pr services, a preemption mechanism preempts resources for the Pr service, by deporting one or more running BE services. The experimental results show that PPPS can reduce the Pr services rejection rate to ~ 0% while minimizing the disturbance that BE services witness such as prolonged waiting times.